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Obtaining an X-Ray of the Zubal Phantom by Monte Carlo Simulation

  • Hassane El BekkouriEmail author
  • Ahmed Dadouch
  • Abdessamad Didi
  • Abdelmajid Maghnouj
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)

Abstract

Radiation transport in matter has attracted great interest since the beginning of the 20th century. High-energy photons, electrons, and positrons penetrate the matter undergoing multiple interactions where his energy is transferred to the atoms and molecules of the material, and secondary particles are produced. By multiple interactions a high energy particle produces a cascade of particles that is usually referred to as a shower. In each interaction, the energy of the particle is reduced and particles can be generated so that the evolution of the shower represents a degradation of the energy. The purpose of this work is to obtain x-ray images of the human body using the Zubal phantom. The transport of the radiation, crossing the phantom and arriving on the detector, is realized using the code MCNP (Monte-Carlo N-Particle transport). The images obtained are comparable to those obtained by a real radiological imaging system.

Keywords

Monte Carlo code Simulation Computed Tomography X-ray Numerical phantom 

References

  1. 1.
    Mettler, F.A., Wiest, P.W., Locken, J.A., Kelsey, C.A.: CT scanning: patterns of use and dose. J. Radiol. Prot. 20, 353–359 (2000)CrossRefGoogle Scholar
  2. 2.
    Shrimpton, P.C., Edyvean, S.: CT scanner dosimetry. Br. J. Radiol. 71, 1–3 (1998)CrossRefGoogle Scholar
  3. 3.
    BfS: Jahresbericht 2003. Annual rep., Bundesamt für Strahlenschutz, Salzgitter (2003)Google Scholar
  4. 4.
    Brix, G., Nagel, H.D., Stamm, G., Veit, R., Lechel, U., Griebel, J., Galanski, M.: Radiation exposure in multi-slice versus single-slice spiral CT: results of a nationwide survey. Eur. Radiol. 13, 1979–1991 (2003).  https://doi.org/10.1007/s00330-003-1883-yCrossRefGoogle Scholar
  5. 5.
    Andreo, P.: Monte Carlo techniques in medical radiation physics. Phys. Med. Biol. 36(861), 920 (1991)Google Scholar
  6. 6.
    Geant4 Collaboration: Introduction to Geant4 (2007). http://geant4.web.cern.ch/geant4
  7. 7.
    Team XBMC: MCNP-general Monte Carlo N-particle transport code, version 5. LA-UR-03-1987 RSICC, Los Alamos, USA (2003)Google Scholar
  8. 8.
    Nelson, W.R., Hirayama, H., Rogers, D.O.W.: The EGS4 code system. Stanford Linear Accelerator Center, Stanford University, USA (1985)Google Scholar
  9. 9.
    Kalender, W.A.: Computed Tomography: Fundamentals, System Technology, Image Quality, Applications. Wiley, New York (2000)zbMATHGoogle Scholar
  10. 10.
    Zubal, I.G., Harrell, C.R., Smith, E.O., Rattner, Z., Gindi, G., Hoffer, P.B.: Computerized three-dimensional segmented human anatomy. Med. Phys. 21(2), 299–302 (1994)CrossRefGoogle Scholar
  11. 11.
    X-6 Monte Carlo Team. a. MCNP, A General Monte Carlo N-Particle Transport Code, Version 1.0: Initial MCNP-6 Release Overview -MCNP6 (2014)Google Scholar
  12. 12.
    Charlie Ma, C.-M.: Calcul Monte Carlo de la dose en radiothérapies. Focus on Radiosurgery, Newsletter No. 1 (2008)Google Scholar
  13. 13.
    Ulam, S.M., von Neumann, J.: On combination of stochastic and deterministic processes. Bull. Am. Math. Soc. 53, 1120 (1947)Google Scholar
  14. 14.
    Raeside, D.E.: Monte Carlo principles and applications. Phys. Med. Biol. 21, 181–197 (1976)CrossRefGoogle Scholar
  15. 15.
    Buffon, G.: Essai d’arithmétique Supplément à la naturelle (1777)Google Scholar
  16. 16.
    Bielajew, A.F., Hirayama, H., Nelson, W.R., Rogers, D.W.O.: History, overview and recent improvements of egs4. Technical Report, PIRS-0436 (1994)Google Scholar
  17. 17.
    Baro, J., Sempau, J., Fernandez-Varea, J.M., Salvat, F.: Penelope: an algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter. Nucl. Instrum. Methods B 100, 31–46 (1995)CrossRefGoogle Scholar
  18. 18.
    Agostinelli, S.: Geant4-a simulation toolkit. Nucl. Instrum. Methods A 506, 250–303 (2003)CrossRefGoogle Scholar
  19. 19.
    Carrier, J.F., Archambault, L., Beaulieu, L., Roy, R.: Validation of geant4, an objectoriented Monte Carlo toolkit, for simulations in medical physics. Med. Phys. 31, 484–492 (2004)CrossRefGoogle Scholar
  20. 20.
    Jan, S.: Gate: a simulation toolkit for pet and spect. Phys. Med. Biol. 49, 4543–4561 (2004)CrossRefGoogle Scholar
  21. 21.
    Berger, L.: Utilisation d’un système d’imagerie portale électronique avec détecteur au slicium amorphe pour vérifier la dose reçue par les patients en radiothérapie. These (2006)Google Scholar
  22. 22.
    Zubal, I.G., Harrell, C.R., Smith, E.O., Smith, A.L., Krischlunas, P.: Two dedicated software, voxel-based, anthropomorphic (torso and head) phantoms. In: Proceedings of the International Workshop, vol. 6, no. 7. National Radiological Protection Board, Chilton, UK, July 1995Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory of Integration System and Technology Advanced (LISTA), Department of Physics, Faculty of ScienceUniversity of Sidi Mohamed Ben AbdellahFezMorocco

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